Direct Image-to-Likelihood for Track-Before-Detect Multi-Bernoulli Filter

Loading...
Thumbnail Image
Author(s)
Murphy, Timothy S.
Holzinger, Marcus J.
Flewelling, Brien R.
Advisor(s)
Editor(s)
Associated Organization(s)
Organizational Unit
Daniel Guggenheim School of Aerospace Engineering
The Daniel Guggenheim School of Aeronautics was established in 1931, with a name change in 1962 to the School of Aerospace Engineering
Series
Supplementary to:
Abstract
This paper aims to apply the random finite set-based multi-Bernoilli filter to frame to- frame tracking of space objects observed in electro optical imagery for space domain awareness applications. First, this paper will review random finite set filters applied to frame to frame tracking and their applications to space. A new likelihood function for space based imagery will be presented, based on the matched filter. A more educated birth model will be proposed which better models potential SO using observer characteristics and object dynamics. Simulation results will explore the range of objects that can be tracked. The final algorithm is able to perform completely uncued detection down to a total object SNR of 5.6 and a per pixel SNR of 1.5. Promising but inconclusive results are shown for total object SNR of 3.35 and per pixel SNR of 0.7.
Sponsor
Date
2016-02
Extent
Resource Type
Text
Resource Subtype
Paper
Rights Statement
Unless otherwise noted, all materials are protected under U.S. Copyright Law and all rights are reserved